The present invention relates to a method for automatic recognition of seismic facies between two horizons, or about a horizon of a geological area, or, more particularly between two horizons or about a horizon defined on a seismic section associated with said geological area.
At present, nearly all geological and geophysical interpretations relative to seismic facies are carried out on an interpretation station and belong to the specialized domain of seismic stratigraphy.
In seismic stratigraphy, it is customary to identify and to represent on a map the variations of seismic facies in a given slice of the geological area to be surveyed (called mapping). The slice may or may not be between two marked horizons.
A seismic facies unit is a group of seismic reflections having configurations, i.e., external shape and internal parameters, which are different from one facies unit to another. The configurations may also be different between two adjacent or consecutive facies units.
The seismic facies units are usually defined by analyzing three families of parameters:
the configuration of the reflections, (e.g., parallel, divergent, sigmoid, etc.), PA1 the external shape (e.g., concave upwards, convex upwards, draped, etc.), PA1 the internal parameters of the reflections (e.g., amplitude, frequency, etc.). PA1 determining a given number of seismic facies to be recognized, PA1 taking a set of seismic trace portions concerning said area, PA1 defining a facies recognition parameter common to all the trace portions and determining the value of said parameter for each of the trace portions of the set, PA1 selecting trace portions from said set, PA1 choosing a one-dimensional neural network containing as many cells as facies to be recognized, each cell being assigned a value of the recognition parameter, PA1 effecting the learning of the neural network via the selected trace portions, so that, when the learning process is complete, each cell corresponds to a facies to be recognized, and so that said facies are gradually ordered, PA1 presenting each trace portion of said set to be processed to the classed and ordered neural network, and PA1 assigning the number of the nearest cell to each of the trace portions presented to the network.
The recognition of the seismic facies in a given geological area is very important because it provides useful information, particularly about the types of sedimentary deposits and the anticipated lithology.
To succeed in recognizing the seismic facies of a given geological area, it is necessary to define each of them first by separately analyzing at least each of the above-mentioned three families of parameters. Next, the parameters should be synthesized in order to gather the maximum data or information about the seismic facies present in the geological area.
The cost of such an analysis and the means to be employed, particularly the data processing means, are excessively high as compared to the results obtained.
In fact, if the seismic facies which one wishes to recognize belong to stratigraphic pinchouts and/or to turbiditic channels, it is very difficult to discriminate between the anomalies when they appear on the usual seismic sections, even if those anomalies are recognized by the well seismic survey as being present in the area concerned, provided that a well is available in the area, which may not be the case.
In EP-0 561 492, a method is described for improving the well logging by making use of neural networks. The particular network described is a layered network. From a statistical standpoint, a layered network is a universal approximator of the boundaries between classes, but, above all it is, a supervised network. In other words, the quantity obtained in the output of the neural network is compared with a quantity known and determined by other methods, until a coincidence or quasi-coincidence is obtained between the quantities.
Since the topological maps due to Kohonen are used in other fields, particularly in the medical field to determine models susceptible to imitate a number of the functions of the brain by reproducing some of its basic structures, geophysicists have attempted to apply them to the field of geophysics.
Particular applications are described in U.S. Pat. No. 5,373,486, which deals with the classification of seismic events by using Kohonen antagonistic networks, in U.S. Pat. No. 5,355,313, which describes the interpretation of aeromagnetic data, and in U.S. Pat. No. 5,181,171, which describes an interactive neural network adapted to detect the first arrivals on the seismic traces.